A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation ModelingSAS Institute, 1 Mar 2013 - 444 sayfa Annotation Structural equation modeling (SEM) has become one of the most important statistical procedures in the social and behavioral sciences. This easy-to-understand guide makes SEM accessible to all userseven those whose training in statistics is limited or who have never used SAS. It gently guides users through the basics of using SAS and shows how to perform some of the most sophisticated data-analysis procedures used by researchers: exploratory factor analysis, path analysis, confirmatory factor analysis, and structural equation modeling. It shows how to perform analyses with user-friendly PROC CALIS, and offers solutions for problems often encountered in real-world research. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures. |
İçindekiler
| 1 | |
Exploratory Factor Analysis | 43 |
Assessing Scale Reliability with Coefficient Alpha | 97 |
Path Analysis | 107 |
Developing Measurement Models with Confirmatory Factor Analysis | 185 |
Structural Equation Modeling | 253 |
Introductdion to SAS Programs SS Logs and SAS Output | 305 |
Data Input | 311 |
Working with Variables and Observations in SAS Datasets | 331 |
Exploring Data with PROC MEANS PROC FREQ PROC PRINT and PROC UNIVARIATE | 357 |
Preparing Scattergrams and Computing Correlations | 385 |
Simplifying PROC CALIS Programs | 401 |
Datasets | 405 |
Critical Values for the ChiSquare Distribution | 411 |
| 413 | |
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alternative appear assess associated assume chapter chi-square column commitment compute confidence construct contains corr correlation covariance create criterion dataset described determine developed directional discussed distribution effect eigenvalue endogenous entered equal equation error estimates example exogenous variables factor analysis factor loadings factor pattern Figure final given greater identify includes Index indices initial input interpret investment Investment Model latent variables loadings manifest matrix means measurement model modification multiple normal Note Notice observed variables obtained option Output parameters participants path coefficient perform possible preceding predicted presented principal component analysis PROC CALIS procedure provides question questionnaire relationship reliability represents residual responses retained RMSEA rotated RULE sample SAS program satisfaction scale scores shows significant simple specified Square standard statement statistics Step structure theoretical variance